[5-4]Rule-based modeling of cellular signalling

Date:2008-05-03

Title：Rule-based modeling of cellular signalling
Speaker：Prof. Vincent Danos （LFCS, Edinburgh University, UK）

Time：3:00pm, May 4
Venue: Lecture room, Lab for Computer Science, Level 3 Building #5, ISCAS

Abstract：
Rule-based modelling is particularly effective for handling the highly combinatorial aspects of cellular signalling. The dynamics is described in terms of interactions between partial complexes, and the ability to write rules with such partial complexes -i.e., not to have to specify all the traits of the entitities partaking in a reaction but just those that matter- is the key to obtaining compact descriptions of what otherwise could be nearly infinite dimensional dynamical systems. This also makes these descriptions easier to read, write and modify.
 
In the course of modelling a particular signalling system it will often happen that more traits matter in a given interaction than previously thought, and one will need to strengthen the conditions under which the corresponding rule may apply. This is a process that we call rule refinement and which we set out in this paper to study. Specifically we present a method to refine rule sets in a way that preserves the implied stochastic semantics.
 
This stochastic semantics is dictated by the number of different ways in which a given rule can be applied to a system (obeying the mass action principle). The refinement formula we obtain explains how to refine rules and which choice of refined rates will lead to a neutral refinement, i.e., one that has the same global activity as the original rule had (and therefore leaves the dynamics unchanged). It has a pleasing mathematical simplicity, and is reusable with little modification across many variants of stochastic graph rewriting. A particular case of the above is the derivation of a maximal refinement which is equivalent to a (possibly infinite) Petri net and can be useful to get a quick approximation of the dynamics and to calibrate models. As we show with examples, refinement is also useful to understand how different subpopulations contribute to the activity of a rule, and to modulate differentially their impact on that activity
 
Bio：
Vincent Danos is a chair professor in the School of Informatics at the University of Edinburgh. He has pursued various lines of research during his career, from mathematical logic and the semantics of programming languages, to probabilistic and agent-based models. He is also Directeur de Recherches at the CNRS, co-Director of the newly created Edinburgh Center of Synthetic Biology, and an external faculty member of the Santa Fe Institute. He has spent last year as a visiting Professor at the Harvard Medical School, in the Department of Systems Biology, and working in a start-up company trying to bring agent based techniques to bear on the representation of complex cellular signalling networks. He is currently leading the development of an efficient bottom-up simulation platform for cellular signalling, that will enable the rapid generation of cellular insight—including causal information—without requiring significant modeling or quantitative capability from the user. He is an editor of the Transactions of computational systems biology (Springer), the on-line journal of Logical Methods in Computer Science, and of IJSI -a recent Chinese international journal. He has published more than 60 papers in various international conferences and journals (LICS, CONCUR, JACM, JSL, TCS) and has been in several programme committees (LICS, POPL, CONCUR, etc). Recent invitations to keynote lectures include APLAS’07, CONCUR’07, TAMC’08, and SOS’08.